Introduction to data analytics and information visualization; methods and metrics for validation; bias-variance trade- off; data visualization and understanding; data preprocessing; supervised learning (classification and regression); unsupervised learning; association rule mining; feature subset selection; metaheuristics; PCA; ANN and Multilayer Perceptron.

The course will address unsupervised learning, supervised learning, association rule mining and feature subset selection problems and introduce various techniques proposed as solutions and present their implementation particularly in the context of operations management. Data Visualization will also be introduced as part of the curriculum.


Among others, probabilistic and statistical methods, clustering algorithms, classification algorithms, multiple linear regression, a priori algorithm, metaheuristics (such as genetic algorithms, simulated annealing, etc.) in the context of feature subset selection will be covered as part of the toolbox that are widely utilized in data mining.

Will be posted at SuCourse

Students will need to model, implement and solve programs in lectures. We will use Jupyter Notebook and Gurobi solver with Python interface.